SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding
Why this work is in the frame
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Bibliographic record
Abstract
We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it